Quantitative Risk Management Concepts, Techniques and Tools

ISBN-10: 0691122555

ISBN-13: 9780691122557

Edition: 2005

Authors: Alexander J. McNeil, R�diger Frey, Paul Embrechts

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The implementation of sound quantitative risk models is a vital concern for all financial institutions, and this trend has accelerated in recent years with regulatory processes such as Basel II. This book provides a comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management and equips readers--whether financial risk analysts, actuaries, regulators, or students of quantitative finance--with practical tools to solve real-world problems. The authors cover methods for market, credit, and operational risk modelling; place standard industry approaches on a more formal footing; and describe recent developments that go beyond, and address main deficiencies of, current practice. The book's methodology draws on diverse quantitative disciplines, from mathematical finance through statistics and econometrics to actuarial mathematics. Main concepts discussed include loss distributions, risk measures, and risk aggregation and allocation principles. A main theme is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. The techniques required derive from multivariate statistical analysis, financial time series modelling, copulas, and extreme value theory. A more technical chapter addresses credit derivatives. Based on courses taught to masters students and professionals, this book is a unique and fundamental reference that is set to become a standard in the field.
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Book details

List price: $115.00
Copyright year: 2005
Publisher: Princeton University Press
Publication date: 10/16/2005
Binding: Hardcover
Pages: 544
Size: 6.25" wide x 9.25" long x 1.25" tall
Weight: 1.980
Language: English

Risk in Perspective
Risk and Randomness
Financial Risk
Measurement and Management
A Brief History of Risk Management
From Babylon to Wall Street
The Road to Regulation
The New Regulatory Framework
Basel II
Solvency 2
Why Manage Financial Risk?
A Societal View
The Shareholder's View
Economic Capital
Quantitative Risk Management
The Nature of the Challenge
QRM for the Future
Basic Concepts in Risk Management
Risk Factors and Loss Distributions
General Definitions
Conditional and Unconditional Loss Distribution
Mapping of Risks: Some Examples
Risk Measurement
Approaches to Risk Measurement
Further Comments on VaR
Other Risk Measures Based on Loss Distributions
Standard Methods for Market Risks
Variance-Covariance Method
Historical Simulation
Monte Carlo
Losses over Several Periods and Scaling
An Illustrative Example
Multivariate Models
Basics of Multivariate Modelling
Random Vectors and Their Distributions
Standard Estimators of Covariance and Correlation
The Multivariate Normal Distribution
Testing Normality and Multivariate Normality
Normal Mixture Distributions
Normal Variance Mixtures
Normal Mean-Variance Mixtures
Generalized Hyperbolic Distributions
Fitting Generalized Hyperbolic Distributions to Data
Empirical Examples
Spherical and Elliptical Distributions
Spherical Distributions
Elliptical Distributions
Properties of Elliptical Distributions
Estimating Dispersion and Correlation
Testing for Elliptical Symmetry
Dimension Reduction Techniques
Factor Models
Statistical Calibration Strategies
Regression Analysis of Factor Models
Principal Component Analysis
Financial Time Series
Empirical Analyses of Financial Time Series
Stylized Facts
Multivariate Stylized Facts
Fundamentals of Time Series Analysis
Basic Definitions
ARMA Processes
Analysis in the Time Domain
Statistical Analysis of Time Series
GARCH Models for Changing Volatility
ARCH Processes
GARCH Processes
Simple Extensions of the GARCH Model
Fitting GARCH Models to Data
Volatility Models and Risk Estimation
Volatility Forecasting
Conditional Risk Measurement
Fundamentals of Multivariate Time Series
Basic Definitions
Analysis in the Time Domain
Multivariate ARMA Processes
Multivariate GARCH Processes
General Structure of Models
Models for Conditional Correlation
Models for Conditional Covariance
Fitting Multivariate GARCH Models
Dimension Reduction in MGARCH
MGARCH and Conditional Risk Measurement
Copulas and Dependence
Basic Properties
Examples of Copulas
Meta Distributions
Simulation of Copulas and Meta Distributions
Further Properties of Copulas
Perfect Dependence
Dependence Measures
Linear Correlation
Rank Correlation
Coefficients of Tail Dependence
Normal Mixture Copulas
Tail Dependence
Rank Correlations
Skewed Normal Mixture Copulas
Grouped Normal Mixture Copulas
Archimedean Copulas
Bivariate Archimedean Copulas
Multivariate Archimedean Copulas
Non-exchangeable Archimedean Copulas
Fitting Copulas to Data
Method-of-Moments using Rank Correlation
Forming a Pseudo-Sample from the Copula
Maximum Likelihood Estimation
Aggregate Risk
Coherent Measures of Risk
The Axioms of Coherence
Coherent Risk Measures Based on Loss Distributions
Coherent Risk Measures as Generalized Scenarios
Mean-VaR Portfolio Optimization
Bounds for Aggregate Risks
The General Frechet Problem
The Case of VaR
Capital Allocation
The Allocation Problem
The Euler Principle and Examples
Economic Justification of the Euler Principle
Extreme Value Theory
Generalized Extreme Value Distribution
Maximum Domains of Attraction
Maxima of Strictly Stationary Time Series
The Block Maxima Method
Threshold Exceedances
Generalized Pareto Distribution
Modelling Excess Losses
Modelling Tails and Measures of Tail Risk
The Hill Method
Simulation Study of EVT Quantile Estimators
Conditional EVT for Financial Time Series
Tails of Specific Models
Domain of Attraction of Frechet Distribution
Domain of Attraction of Gumbel Distribution
Mixture Models
Point Process Models
Threshold Exceedances for Strict White Noise
The POT Model
Self-Exciting Processes
A Self-Exciting POT Model
Multivariate Maxima
Multivariate Extreme Value Copulas
Copulas for Multivariate Minima
Copula Domains of Attraction
Modelling Multivariate Block Maxima
Multivariate Threshold Exceedances
Threshold Models Using EV Copulas
Fitting a Multivariate Tail Model
Threshold Copulas and Their Limits
Credit Risk Management
Introduction to Credit Risk Modelling
Credit Risk Models
The Nature of the Challenge
Structural Models of Default
The Merton Model
Pricing in Merton's Model
The KMV Model
Models Based on Credit Migration
Multivariate Firm-Value Models
Threshold Models
Notation for One-Period Portfolio Models
Threshold Models and Copulas
Industry Examples
Models Based on Alternative Copulas
Model Risk Issues
The Mixture Model Approach
One-Factor Bernoulli Mixture Models
Asymptotics for Large Portfolios
Threshold Models as Mixture Models
Model-Theoretic Aspects of Basel II
Model Risk Issues
Monte Carlo Methods
Basics of Importance Sampling
Application to Bernoulli-Mixture Models
Statistical Inference for Mixture Models
Exchangeable Bernoulli-Mixture Models
Mixture Models as GLMMs
One-Factor Model with Rating Effect
Dynamic Credit Risk Models
Credit Derivatives
Single-Name Credit Derivatives
Portfolio Credit Derivatives
Mathematical Tools
Random Times and Hazard Rates
Modelling Additional Information
Doubly Stochastic Random Times
Financial and Actuarial Pricing of Credit Risk
Physical and Risk-Neutral Probability Measure
Risk-Neutral Pricing and Market Completeness
Martingale Modelling
The Actuarial Approach to Credit Risk Pricing
Pricing with Doubly Stochastic Default Times
Recovery Payments of Corporate Bonds
The Model
Pricing Formulas
Affine Models
Basic Results
The CIR Square-Root Diffusion
Conditionally Independent Defaults
Reduced-Form Models for Portfolio Credit Risk
Conditionally Independent Default Times
Examples and Applications
Copula Models
Definition and General Properties
Factor Copula Models
Default Contagion in Reduced-Form Models
Default Contagion and Default Dependence
Information-Based Default Contagion
Interacting Intensities
Operational Risk and Insurance Analytics
Operational Risk in Perspective
A New Risk Class
The Elementary Approaches
Advanced Measurement Approaches
Operational Loss Data
Elements of Insurance Analytics
The Case for Acturaial Methodology
The Total Loss Amount
Approximations and Panjer Recursion
Poisson Mixtures
Tails of Aggregate Loss Distributions
The Homogeneous Poisson Process
Processes Related to the Poisson Process
Miscellaneous Definitions and Results
Type of Distribution
Generalized Inverses and Quantiles
Karamata's Theorem
Probability Distributions
Generalized Inverse Gaussian
Inverse Gamma
Negative Binomial
Likelihood Inference
Maximum Likelihood Estimators
Asymptotic Results: Scalar Parameter
Asymptotic Results: Vector of Parameters
Wald Test and Confidence Intervals
Likelihood Ratio Test and Confidence Intervals
Akaike Information Criterion
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